Paper Info
Title
3D map building method with mobile mapping system in indoor environments
Abstract
This paper presents three dimensional (3D) map building method for the intelligent vehicles based on accurate indoor localization using a mobile mapping system (MMS) that is equipped with perception sensors consist of a wheel odometer, a laser range finder (LRF), and two projected texture stereo (PTS) cameras. The environmental data measured by perception sensors are stored in the node units according to a certain distance interval. In order to estimate the positions of the MMS using the relationship among nodes, the localization method is divided into two parts, front-end (map-based scan matching) and back-end (graph-based optimization). The estimated positions are used to build the grid-based map and the point cloud dataset, respectively as the 2D and the 3D maps through the mapping process (Bayesian model). An experiment has been performed in office environment (indoor) to verify the effectiveness of the proposed method. Experimental results show the high precision of 3D point cloud dataset that can be used for various applications including navigation of intelligent vehicles and pedestrians in indoor evironments.
Year
DOI
Venue
2013
10.1109/ICAR.2013.6766588
ICAR
Keywords
Field
DocType
bayes methods,slam (robots),cameras,graph theory,laser ranging,mobile robots,optimisation,robot vision,3d map building method,3d point cloud dataset,bayesian model,lrf,mms,pts cameras,environmental data,graph-based optimization,grid-based map,indoor environments,indoor localization,intelligent vehicles,laser range finder,mapping process,mobile mapping system,pedestrians,perception sensors,texture stereo cameras,wheel odometer
Graph theory,Computer vision,Bayesian inference,Computer science,Artificial intelligence,Environmental data,Point cloud,Mobile mapping,Mobile robot,Grid,Odometer
Conference
Citations 
PageRank 
References 
4
0.47
8
Authors
2
Name
Order
Citations
PageRank
Yu-Cheol Lee1368.05
Seunghwan Park2262.67